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Structural strength analysis of loess based on genetic algorithm and the prediction and optimization of coal mine gas emission

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Arabian Journal of Geosciences 2021年第15期14卷 1-9页

作者： Xie, Xiumei Du, Xuedong Wang, Binguo College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Provincial Computer Experimental Teaching Demonstrating Center of Shandong Province Qingdao China Key Laboratory of Information Technology for Intelligence Mines in Shandong Province Qingdao China

Loess is a special kind of broken granular material, and its structure is closely related to its physical and mechanical properties. From the macroscopic and microscopic study of loess to the multi-step comprehensive study of the development process of loess, in the study of the effect of medium on the structural strength characteristics of loess, many scholars have deeply discussed. At present, the main research direction of loess deformation and fracture is to clarify the nature and mechanism of macro-deformation of loess, especially from the change of microstructure. Therefore, it is of great scientific significance to study and explore the relationship between structure and soil shear strength, which can also provide guidance for specific engineering practice theory. In this paper, the deformation and microstructure characteristics of loess are analyzed according to the water content, and the Q3 loess in Y City is mainly studied. Based on genetic algorithm, the relationship between soil structure and deformation and failure characteristics is studied. However, JRBF network has many uncertain parameters, so it is difficult to determine the optimal value. Therefore, using RBF neural network to predict mine gas emission has certain limitations. In this paper, we use genetic algorithm to optimize and simplify the structure and parameters of RBF network and establish a GA-RBF prediction model to predict the amount of gas emission. The model keeps the best value of network parameters based on the principle of advantages and disadvantages and uses transformation and other operations to select escape algorithm. The simulation results show that the prediction accuracy of ca-rbf is better than that of traditional RBF, and the training speed is also greatly improved. According to the research of genetic algorithm, it is applied to loess structure and mine gas emission, which can effectively improve the loess structure and mine gas emission.

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Measuring "why" in recommender systems: A comprehensive survey on the evaluation of explainable recommendation

arXiv

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arXiv 2022年

作者： Chen, Xu Zhang, Yongfeng Wen, Ji-Rong Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China China Department of Computer Science Rutgers University United States

Explainable recommendation has shown its great advantages for improving recommendation persuasiveness, user satisfaction, system transparency and among others. A fundamental problem of explainable recommendation is how to evaluate the explanations. In the past few years, various evaluation strategies have been proposed. However, they are scattered in different papers, and there lacks a systematic and detailed comparison between them. To bridge this gap, in this paper, we comprehensively review the previous work, and provide different taxonomies according to the evaluation perspectives and evaluation methods. Beyond summarization, we also analyze the (dis)advantages of existing evaluation methods, and provide a series of guidelines on how to select them. The contents of this survey are concluded from more than 100 papers from top-tier conferences like IJCAI, AAAI, TheWebConf, Recsys, UMAP and IUI, and the complete comparisons are presented at https://***/sheets/VKrpYTcwVH6KXgdy/MO DOC/. With this survey, we finally aim to provide a clear and comprehensive review on the evaluation of explainable recommendation. © 2022, CC BY.

关键词： Surveys

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Hesitant Fuzzy Sublattices of Lattices

Hesitant Fuzzy Sublattices of Lattices

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Chunhui Liu Yanhui Bai Jing Wu School of Educational Science Chifeng University Chifeng P. R. China School of Mathematics and Computer Science Chifeng University Chifeng P. R. China Mathematics Teaching and Research Group No.4 Middle School of Chifeng Chifeng P. R. China

In this paper, sublattices problem of lattices is studied by using the method and principle of hesitant fuzzy sets. The concept of hesitant fuzzy sublattices of lattices is introduced and its properties are investigated. The relationship between hesitant fuzzy sublattices and sublattices is given. It is proved that hesitant fuzzy intersection and direct product of some hesitant fuzzy sublattices, homomorphism image and inverse image of a hesitant fuzzy sublattice are also hesitant fuzzy sublattice. At the same time, a condition which makes hesitant fuzzy union of some hesitant fuzzy sublattices to be a hesitant fuzzy sublattice is obtained.

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FABind: fast and accurate protein-ligand binding 23

FABind: fast and accurate protein-ligand binding

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Qizhi Pei Kaiyuan Gao Lijun Wu Jinhua Zhu Yingce Xia Shufang Xie Tao Qin Kun He Tie-Yan Liu Rui Yan Gaoling School of Artificial Intelligence Renmin University of China and Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Microsoft Research AI4Science School of Information Science and Technology University of Science and Technology of China Gaoling School of Artificial Intelligence Renmin University of China Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose FABind, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. FABind incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed FABind demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at Github https://***/QizhiPei/FABind.

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Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection

arXiv

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arXiv 2024年

作者： Suvon, Mohammod N.I. Tripathi, Prasun C. Fan, Wenrui Zhou, Shuo Liu, Xianyuan Alabed, Samer Osmani, Venet Swift, Andrew J. Chen, Chen Lu, Haiping Department of Computer Science University of Sheffield Sheffield United Kingdom Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom Department of Infection Immunity and Cardiovascular Disease University of Sheffield Sheffield United Kingdom Department of Clinical Radiology Sheffield Teaching Hospitals Sheffield United Kingdom INSIGNEO Institute for in Silico Medicine University of Sheffield Sheffield United Kingdom Information School University of Sheffield Sheffield United Kingdom Department of Computer Science IITRAM Gujarat India Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computing Imperial College London London United Kingdom

Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI) primarily focus on applying machine learning techniques to a single data modality, e.g. cardiac magnetic resonance imaging (MRI). Despite their potential, these approaches often fall short especially when the size of labeled patient data is limited, a common challenge in the medical domain. Furthermore, only a few studies have explored multimodal methods to study CHDI, which mostly rely on costly modalities such as cardiac MRI and echocardiogram. In response to these limitations, we propose a novel multimodal variational autoencoder (CardioVAEX,G) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training on a large unlabeled dataset. Specifically, CardioVAEX,G introduces a novel tristream pre-training strategy to learn both shared and modality-specific features, thus enabling fine-tuning with both unimodal and multimodal datasets. We pre-train CardioVAEX,G on a large, unlabeled dataset of 50, 982 subjects from a subset of MIMIC database and then fine-tune the pre-trained model on a labeled dataset of 795 subjects from the ASPIRE registry. Comprehensive evaluations against existing methods show that CardioVAEX,G offers promising performance (AUROC = 0.79 and Accuracy = 0.77), representing a significant step forward in non-invasive prediction of CHDI. Our model also excels in producing fine interpretations of predictions directly associated with clinical features, thereby supporting clinical decision-making. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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An Adaptable SVM Model for Abnormalities Detection in Chest X-ray Reports

An Adaptable SVM Model for Abnormalities Detection in Chest ...

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Information Technology for Education and Development (ITED)

作者： Abimbola Rhoda Iyanda Omolara Aminat Ogungbe Adeniyi Sunday Aderibigbe Department of Computer Science and Engineering Obafemi Awolowo University Ile-Ife Nigeria Department of Radiology Obafemi Awolowo University Teaching Hospital Complex Ile-Ife Nigeria

In Nigeria, prose format is used to present and perform analysis on chest x-ray reports and this often results in delayed response from the clinicians. Therefore, with a view to developing a system for analyzing chest x-ray reports for diagnosing cardiomegaly, linear support vector machine algorithm was utilized to formulate an adaptable model with a train-test split of 70:30 for six hundred and fifty (650) de-identified patients' information. Attributes relevant to cardiomegaly from the collected dataset were extracted using Term frequency/inverse document frequency technique. This work provides an adequate requirement for diagnosis design with accuracy of 93.69%. Its implementation in software application has the potential to reduce delay in attending to patients and can also help the clinicians focus on the findings from chest x-ray reports.

关键词： Analytical models Adaptation models Computational modeling Support vector machine classification X-rays Software X-ray imaging

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Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

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IEEE Transactions on Multimedia 2025年

作者： Fu, Congrui Yuan, Hui Jiang, Shiqi Zhang, Guanghui Shen, Liquan Hamzaoui, Raouf Center of Experimental Teaching China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China Shandong University School of software Shandong Jinan China Shandong University School of Computer Science and Technology China Shanghai University School of Communication and Information Engineering Shanghai China De Montfort University School of Engineering and Sustainable Development LeicesterLE1 9BH United Kingdom

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. Our method combines long-term global information and short-term local information from the video to better extract complete and accurate spatial-temporal information. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This provides a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms open source state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.2 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectiv

关键词： Long short-term memory

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Business Process Model Patterns: Classification and Repository of Published Work

Business Process Model Patterns: Classification and Reposito...

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40 Years EMISA 2019

作者： Fellmann, Michael Koschmider, Agnes Laue, Ralf Schoknecht, Andreas Vetter, Arthur Institute of Computer Science University of Rostock Rostock Germany KIT Institute for Applied Informatics and Formal Description Methods Build. 5.20 Karlsruhe76133 Germany Department of Computer Science Westsächsische Hochschule Zwickau Zwickau Germany

ISBN: (纸本)9783885796985

Patterns have gained widespread interest and acceptance in various domains. Originating from architecture, patterns are nowadays also suggested as solution templates for various problems related to Business Process Management. Due to the abundance of pattern works, getting an overview on available works or searching for specific patterns can be very difficult. What is missing is an instrument for easy exploration and search. To mitigate this problem, we classified published works on pattern and developed a repository for business process model pattern works. The repository is publicly available and enables browsing and filtering of pattern works according to criteria recently developed by analyzing 280 pattern works. © 2020 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Business Process Management Pattern Repository Process Model Patterns

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Data Cleaning Process for mHealth Log Data to Inform Health Worker Performance

Data Cleaning Process for mHealth Log Data to Inform Health ...

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作者： Savai, Simon Muhindi Hasan, Md Kamrul Kamano, Jemimah Misoi, Lawrence Wakholi, Peter Were, Martin C. Institute of Biomedical Informatics Moi University Eldoret Kenya Department of Computer Science Vanderbilt University TN United States School of Medicine Moi University Eldoret Kenya Moi Teaching and Referral Hospital Eldoret Kenya School of Computing and Informatics Technology Makerere University Kampala Uganda Department of Biomedical Informatics VUMC Nashville TN United States

ISBN: (纸本)9781643682907

Log data, captured during use of mobile health (mHealth) applications by health providers, can play an important role in informing nature of user engagement with the application. The log data can also be employed in understanding health provider work patterns and performance. However, given that these logs are raw data, they require robust cleaning and curation if accurate conclusions are to be derived from analyzing them. This paper describes a systematic data cleaning process for mHealth-derived logs based on Broeck's framework, which involves iterative screening, diagnosis, and treatment of the log data. For this study, log data from the demonstrative mUzima mHealth application are used. The employed data cleaning process uncovered data inconsistencies, duplicate logs, missing data within logs that required imputation, among other issues. After the data cleaning process, only 39,229 log records out of the initial 91,432 usage logs (42.9%) could be included in the final dataset suitable for analyses of health provider work patterns. This work highlights the significance of having a systematic data cleaning approach for log data to derive useful information on health provider work patterns and performance. © 2022 The authors and IOS Press.

关键词： Developing countries

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User Behavior Monitoring Mechanism of Online Shopping Processes based on Hierarchical Colored Petri Net

User Behavior Monitoring Mechanism of Online Shopping Proces...

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Chinese Automation Congress (CAC)

作者： Qi Guo Wangyang Yu Zhuojing Ma Jinming Kong Key Laboratory of Modern Teaching Technology Ministry of Education School of Computer Science Shaanxi Normal University Xi’an China School of Computer Science Shaanxi Normal University Xi’an China

ISBN: (纸本)9781665426480

Online shopping has developed very rapidly with the rise of economy, but due to its open network environment, many security issues have also appeared. Among them, behavior risk has become a new security challenge in the online shopping process. Thus, this paper proposes a monitoring mechanism for the behavior risk of online shopping users. Once there is risky information in the user’s session, it can be monitored and processed in time. In this paper, the modeling method based on Hierarchical Colored Petri Net (HCPN) is used to depict the monitoring mechanism, and the CPNTools is used to generate the state space diagram to validate the correctness of the model. This monitoring mechanism supplements the analysis theory of user behavior risk during online shopping processes. The proposed method can be used to protect consumers’ legitimate interests in real-time.

关键词： Automation Petri nets Real-time systems Electronic commerce Security Monitoring

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